Ettore Di Giacinto
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README.md
π« llama-cli
llama-cli is a straightforward golang CLI interface for llama.cpp, providing a simple API and a command line interface that allows text generation using a GPT-based model like llama directly from the terminal.
Container images
To begin, run:
docker run -ti --rm quay.io/go-skynet/llama-cli:v0.3 --instruction "What's an alpaca?" --topk 10000
You will receive a response like the following:
An alpaca is a member of the South American Camelid family, which includes the llama, guanaco and vicuΓ±a. It is a domesticated species that originates from the Andes mountain range in South America. Alpacas are used in the textile industry for their fleece, which is much softer than wool. Alpacas are also used for meat, milk, and fiber.
Basic usage
To use llama-cli, specify a pre-trained GPT-based model, an input text, and an instruction for text generation. llama-cli takes the following arguments when running from the CLI:
llama-cli --model <model_path> --instruction <instruction> [--input <input>] [--template <template_path>] [--tokens <num_tokens>] [--threads <num_threads>] [--temperature <temperature>] [--topp <top_p>] [--topk <top_k>]
Parameter | Environment Variable | Default Value | Description |
---|---|---|---|
template | TEMPLATE | A file containing a template for output formatting (optional). | |
instruction | INSTRUCTION | Input prompt text or instruction. "-" for STDIN. | |
input | INPUT | - | Path to text or "-" for STDIN. |
model | MODEL_PATH | The path to the pre-trained GPT-based model. | |
tokens | TOKENS | 128 | The maximum number of tokens to generate. |
threads | THREADS | NumCPU() | The number of threads to use for text generation. |
temperature | TEMPERATURE | 0.95 | Sampling temperature for model output. ( values between 0.1 and 1.0 ) |
top_p | TOP_P | 0.85 | The cumulative probability for top-p sampling. |
top_k | TOP_K | 20 | The number of top-k tokens to consider for text generation. |
context-size | CONTEXT_SIZE | 512 | Default token context size. |
alpaca | ALPACA | true | Set to true for alpaca models. |
Here's an example of using llama-cli
:
llama-cli --model ~/ggml-alpaca-7b-q4.bin --instruction "What's an alpaca?"
This will generate text based on the given model and instruction.
Advanced usage
llama-cli
also provides an API for running text generation as a service. The model will be pre-loaded and kept in memory.
Example of starting the API with docker
:
docker run -p 8080:8080 -ti --rm quay.io/go-skynet/llama-cli:v0.3 api --context-size 700 --threads 4
And you'll see:
βββββββββββββββββββββββββββββββββββββββββββββββββββββ
β Fiber v2.42.0 β
β http://127.0.0.1:8080 β
β (bound on host 0.0.0.0 and port 8080) β
β β
β Handlers ............. 1 Processes ........... 1 β
β Prefork ....... Disabled PID ................. 1 β
βββββββββββββββββββββββββββββββββββββββββββββββββββββ
You can control the API server options with command line arguments:
llama-cli api --model <model_path> [--address <address>] [--threads <num_threads>]
The API takes takes the following:
Parameter | Environment Variable | Default Value | Description |
---|---|---|---|
model | MODEL_PATH | The path to the pre-trained GPT-based model. | |
threads | THREADS | CPU cores | The number of threads to use for text generation. |
address | ADDRESS | :8080 | The address and port to listen on. |
context-size | CONTEXT_SIZE | 512 | Default token context size. |
alpaca | ALPACA | true | Set to true for alpaca models. |
Once the server is running, you can make requests to it using HTTP. For example, to generate text based on an instruction, you can send a POST request to the /predict
endpoint with the instruction as the request body:
curl --location --request POST 'http://localhost:8080/predict' --header 'Content-Type: application/json' --data-raw '{
"text": "What is an alpaca?",
"topP": 0.8,
"topK": 50,
"temperature": 0.7,
"tokens": 100
}'
Note: The API doesn't inject a template for talking to the instance, while the CLI does. You have to use a prompt similar to what's described in the standford-alpaca docs: https://github.com/tatsu-lab/stanford_alpaca#data-release, for instance:
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response:
Using other models
You can use the lite images ( for example quay.io/go-skynet/llama-cli:v0.3-lite
) that don't ship any model, and specify a model binary to be used for inference with --model
.
13B and 30B models are known to work:
# Download the model image, extract the model
# Use the model with llama-cli
docker run -v $PWD:/models -p 8080:8080 -ti --rm quay.io/go-skynet/llama-cli:v0.3-lite api --model /models/model.bin
Golang client API
The llama-cli
codebase has also a small client in go that can be used alongside with the api:
package main
import (
"fmt"
client "github.com/go-skynet/llama-cli/client"
)
func main() {
cli := client.NewClient("http://ip:30007")
out, err := cli.Predict("What's an alpaca?")
if err != nil {
panic(err)
}
fmt.Println(out)
}
Kubernetes
You can run the API directly in Kubernetes:
kubectl apply -f https://raw.githubusercontent.com/go-skynet/llama-cli/master/kubernetes/deployment.yaml
Build locally
Pre-built images might fit well for most of the modern hardware, however you can and might need to build the images manually.
In order to build the llama-cli
container image locally you can use docker
:
# build the image as "alpaca-image"
docker run --privileged -v /var/run/docker.sock:/var/run/docker.sock --rm -t -v "$(pwd)":/workspace -v earthly-tmp:/tmp/earthly:rw earthly/earthly:v0.7.2 +image --IMAGE=alpaca-image
# run the image
docker run alpaca-image --instruction "What's an alpaca?"
Or build the binary with:
# build the image as "alpaca-image"
docker run --privileged -v /var/run/docker.sock:/var/run/docker.sock --rm -t -v "$(pwd)":/workspace -v earthly-tmp:/tmp/earthly:rw earthly/earthly:v0.7.2 +build
# run the binary
./llama-cli --instruction "What's an alpaca?"